5 lessons from 5 interviews on AI agents and web scraping

Short summary

Five web scraping and AI practitioners share hard-won lessons: write simple, testable code over defensive patterns; developer leverage shifts to design, schema, and evaluation; AI amplifies judgment rather than replacing it; define what 'good data' means before deploying AI-assisted QA; and keep agent skill instructions concise, as overly detailed guides paradoxically degrade agent performance.

• •Maintainability is part of correctness—write simple code now with tests, not defensive code for future cases
• •Developers shift from implementation to design: deciding what should exist, defining schemas, and evaluating output
• •AI superpowers come from engineering judgment moving faster, not from blind delegation or absent judgment
• •Data quality cannot be fixed by AI alone—schema, field definitions, and quality standards must come first
• •Concise agent instructions beat verbose ones; over-detailed guides can make agents less effective by obscuring simple solutions

Generated with AI, which can make mistakes.